Data Science Capstone project
Course 9 project

Imports

!pip install folium

import pandas as pd
import numpy as np
import requests
import folium
from sklearn.cluster import KMeans
import matplotlib.cm as cm
import matplotlib.colors as colors

Requirement already satisfied: folium in /home/denys/anaconda3/lib/python3.8/site-packages (0.12.0)
Requirement already satisfied: numpy in /home/denys/anaconda3/lib/python3.8/site-packages (from folium) (1.19.2)
Requirement already satisfied: branca>=0.3.0 in /home/denys/anaconda3/lib/python3.8/site-packages (from folium) (0.4.2)
Requirement already satisfied: jinja2>=2.9 in /home/denys/anaconda3/lib/python3.8/site-packages (from folium) (2.11.2)
Requirement already satisfied: requests in /home/denys/anaconda3/lib/python3.8/site-packages (from folium) (2.24.0)
Requirement already satisfied: MarkupSafe>=0.23 in /home/denys/anaconda3/lib/python3.8/site-packages (from jinja2>=2.9->folium) (1.1.1)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /home/denys/anaconda3/lib/python3.8/site-packages (from requests->folium) (1.25.11)
Requirement already satisfied: certifi>=2017.4.17 in /home/denys/anaconda3/lib/python3.8/site-packages (from requests->folium) (2020.6.20)
Requirement already satisfied: idna<3,>=2.5 in /home/denys/anaconda3/lib/python3.8/site-packages (from requests->folium) (2.10)
Requirement already satisfied: chardet<4,>=3.0.2 in /home/denys/anaconda3/lib/python3.8/site-packages (from requests->folium) (3.0.4)

Load and wrangle indexes data from Wikipedia

# Load raw data from Wikipedia
df = pd.read_html('https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M')[0]


# Remove NA Boroughs
is_not_na = df['Borough'] != 'Not assigned'
df1 = df[is_not_na]


# Treat NA Neighbourhoods
df2 = df1.copy()
for index, row in df2.iterrows():
    if df2.loc[index,'Neighbourhood'] == 'Not assigned':
       df2.loc[index,'Neighbourhood'] = df2.loc[index,'Borough']


# Note: Rows are already merged by Postal Code

View DF shape

df2.shape

(103, 3)

Load coordinates data

coordinates_df = pd.read_csv('https://cocl.us/Geospatial_data')
coordinates_df.head()

	Postal Code	Latitude	Longitude
0	M1B	43.806686	-79.194353
1	M1C	43.784535	-79.160497
2	M1E	43.763573	-79.188711
3	M1G	43.770992	-79.216917
4	M1H	43.773136	-79.239476

Add Coordinates

df3 = pd.merge(left=df2, right=coordinates_df, left_on='Postal Code', right_on='Postal Code')
print(df3.shape)
df3.head()

(103, 5)

	Postal Code	Borough	Neighbourhood	Latitude	Longitude
0	M3A	North York	Parkwoods	43.753259	-79.329656
1	M4A	North York	Victoria Village	43.725882	-79.315572
2	M5A	Downtown Toronto	Regent Park, Harbourfront	43.654260	-79.360636
3	M6A	North York	Lawrence Manor, Lawrence Heights	43.718518	-79.464763
4	M7A	Downtown Toronto	Queen's Park, Ontario Provincial Government	43.662301	-79.389494

CLIENT_ID = 'SBPPJXXVK3DNXUMMAEXGNQ5DRZDY5DAPBQB5RS3VDEVBIYAD' # your Foursquare ID
CLIENT_SECRET = '{hidden}' # your Foursquare Secret
VERSION = '20180605' # Foursquare API version
LIMIT = 100 # A default Foursquare API limit value

def getNearbyVenues(names, latitudes, longitudes, radius=500):
    
    venues_list=[]
    for name, lat, lng in zip(names, latitudes, longitudes):
        #print(name)
            
        # create the API request URL
        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(
            CLIENT_ID, 
            CLIENT_SECRET, 
            VERSION, 
            lat, 
            lng, 
            radius, 
            LIMIT)
            
        # make the GET request
        results = requests.get(url).json()["response"]['groups'][0]['items']
        
        # return only relevant information for each nearby venue
        venues_list.append([(
            name, 
            lat, 
            lng, 
            v['venue']['name'], 
            v['venue']['location']['lat'], 
            v['venue']['location']['lng'],  
            v['venue']['categories'][0]['name']) for v in results])

    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])
    nearby_venues.columns = ['Neighborhood', 
                  'Neighborhood Latitude', 
                  'Neighborhood Longitude', 
                  'Venue', 
                  'Venue Latitude', 
                  'Venue Longitude', 
                  'Venue Category']
    
    return(nearby_venues)

venues = getNearbyVenues(names=df3['Neighbourhood'],
                                   latitudes=df3['Latitude'],
                                   longitudes=df3['Longitude'],
                                   radius=500
                                  )

print(venues.shape)
venues.head()

(2117, 7)

	Neighborhood	Neighborhood Latitude	Neighborhood Longitude	Venue	Venue Latitude	Venue Longitude	Venue Category
0	Parkwoods	43.753259	-79.329656	Brookbanks Park	43.751976	-79.332140	Park
1	Parkwoods	43.753259	-79.329656	Variety Store	43.751974	-79.333114	Food & Drink Shop
2	Victoria Village	43.725882	-79.315572	Victoria Village Arena	43.723481	-79.315635	Hockey Arena
3	Victoria Village	43.725882	-79.315572	Portugril	43.725819	-79.312785	Portuguese Restaurant
4	Victoria Village	43.725882	-79.315572	Tim Hortons	43.725517	-79.313103	Coffee Shop

map1 = folium.Map(location=[df3.loc[0, 'Latitude'], df3.loc[0, 'Longitude']], zoom_start=11)

# add markers to map
for lat, lng, label in zip(venues['Venue Latitude'], venues['Venue Longitude'], venues['Neighborhood']):
    label = folium.Popup(label, parse_html=True)
    folium.CircleMarker(
        [lat, lng],
        radius=5,
        popup=label,
        color='blue',
        fill=True,
        fill_color='#3186cc',
        fill_opacity=0.7,
        parse_html=False).add_to(map1)  
    
map1

Make this Notebook Trusted to load map: File -> Trust Notebook

We see more rural Neighbourhoods have lower density, so it's hard to compare them to more central ones as we're using a single Radius parameter for all of them. Let's try to focus on the ones that have a 'Toronto' in their Boroughs for now.

df31 = df3[df3['Borough'].str.contains('Toronto')]
venues31 = getNearbyVenues(names=df31['Neighbourhood'],
                                   latitudes=df31['Latitude'],
                                   longitudes=df31['Longitude']
                                  )

map31 = folium.Map(location=[df31.loc[2, 'Latitude'], df31.loc[2, 'Longitude']], zoom_start=11)

# add markers to map
for lat, lng, label in zip(venues31['Venue Latitude'], venues31['Venue Longitude'], venues31['Neighborhood']):
    label = folium.Popup(label, parse_html=True)
    folium.CircleMarker(
        [lat, lng],
        radius=5,
        popup=label,
        color='blue',
        fill=True,
        fill_color='#3186cc',
        fill_opacity=0.7,
        parse_html=False).add_to(map31)  
    
map31

Make this Notebook Trusted to load map: File -> Trust Notebook

# one hot encoding
toronto_onehot = pd.get_dummies(venues31[['Venue Category']], prefix="", prefix_sep="")

# add neighborhood column back to dataframe
toronto_onehot['Neighborhood'] = venues31['Neighborhood'] 

# move neighborhood column to the first column
nbh = toronto_onehot['Neighborhood']
toronto_onehot.drop(labels=['Neighborhood'], axis=1,inplace = True)
toronto_onehot.insert(0, 'Neighborhood', nbh)

toronto_onehot.head()

	Neighborhood	Airport	Airport Food Court	Airport Lounge	Airport Service	Airport Terminal	American Restaurant	Antique Shop	Aquarium	Art Gallery	...	Tibetan Restaurant	Toy / Game Store	Trail	Train Station	Vegetarian / Vegan Restaurant	Video Game Store	Vietnamese Restaurant	Wine Bar	Wine Shop	Yoga Studio
0	Regent Park, Harbourfront	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
1	Regent Park, Harbourfront	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2	Regent Park, Harbourfront	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
3	Regent Park, Harbourfront	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
4	Regent Park, Harbourfront	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

5 rows × 234 columns

toronto_grouped = toronto_onehot.groupby('Neighborhood').mean().reset_index()
toronto_grouped

	Neighborhood	Airport	Airport Food Court	Airport Lounge	Airport Service	Airport Terminal	American Restaurant	Antique Shop	Aquarium	Art Gallery	...	Tibetan Restaurant	Toy / Game Store	Trail	Train Station	Vegetarian / Vegan Restaurant	Video Game Store	Vietnamese Restaurant	Wine Bar	Wine Shop	Yoga Studio
0	Berczy Park	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.00	0.017544	...	0.000000	0.000000	0.000000	0.00	0.017544	0.000000	0.000000	0.000000	0.000000	0.000000
1	Brockton, Parkdale Village, Exhibition Place	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.00	0.000000	...	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
2	Business reply mail Processing Centre, South C...	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.00	0.000000	...	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
3	CN Tower, King and Spadina, Railway Lands, Har...	0.066667	0.066667	0.133333	0.2	0.133333	0.000000	0.000000	0.00	0.000000	...	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
4	Central Bay Street	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.00	0.000000	...	0.000000	0.000000	0.000000	0.00	0.016393	0.000000	0.000000	0.016393	0.000000	0.016393
5	Christie	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.00	0.000000	...	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
6	Church and Wellesley	0.000000	0.000000	0.000000	0.0	0.000000	0.012658	0.000000	0.00	0.000000	...	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.012658	0.000000	0.000000	0.025316
7	Commerce Court, Victoria Hotel	0.000000	0.000000	0.000000	0.0	0.000000	0.040000	0.000000	0.00	0.010000	...	0.000000	0.000000	0.000000	0.00	0.020000	0.000000	0.000000	0.010000	0.000000	0.000000
8	Davisville	0.000000	0.000000	0.000000	0.0	0.000000	0.030303	0.000000	0.00	0.000000	...	0.000000	0.030303	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
9	Davisville North	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.00	0.000000	...	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
10	Dufferin, Dovercourt Village	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.00	0.000000	...	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
11	First Canadian Place, Underground city	0.000000	0.000000	0.000000	0.0	0.000000	0.030000	0.000000	0.00	0.010000	...	0.000000	0.000000	0.000000	0.01	0.010000	0.000000	0.000000	0.010000	0.000000	0.000000
12	Forest Hill North & West, Forest Hill Road Park	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.00	0.000000	...	0.000000	0.000000	0.250000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
13	Garden District, Ryerson	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.00	0.010000	...	0.000000	0.000000	0.000000	0.00	0.000000	0.010000	0.010000	0.010000	0.000000	0.000000
14	Harbourfront East, Union Station, Toronto Islands	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.05	0.010000	...	0.000000	0.000000	0.000000	0.01	0.010000	0.000000	0.000000	0.010000	0.000000	0.000000
15	High Park, The Junction South	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.041667	0.00	0.000000	...	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
16	India Bazaar, The Beaches West	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.00	0.000000	...	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
17	Kensington Market, Chinatown, Grange Park	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.00	0.000000	...	0.000000	0.000000	0.000000	0.00	0.046875	0.000000	0.046875	0.015625	0.000000	0.000000
18	Lawrence Park	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.00	0.000000	...	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
19	Little Portugal, Trinity	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.00	0.023256	...	0.000000	0.000000	0.000000	0.00	0.046512	0.000000	0.046512	0.023256	0.000000	0.023256
20	Moore Park, Summerhill East	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.00	0.000000	...	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
21	North Toronto West, Lawrence Park	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.00	0.000000	...	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.047619
22	Parkdale, Roncesvalles	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.00	0.000000	...	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
23	Queen's Park, Ontario Provincial Government	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.00	0.000000	...	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.028571
24	Regent Park, Harbourfront	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.021739	0.00	0.021739	...	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.021739	0.021739
25	Richmond, Adelaide, King	0.000000	0.000000	0.000000	0.0	0.000000	0.020619	0.000000	0.00	0.010309	...	0.000000	0.000000	0.000000	0.00	0.010309	0.000000	0.000000	0.000000	0.000000	0.000000
26	Rosedale	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.00	0.000000	...	0.000000	0.000000	0.250000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
27	Roselawn	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.00	0.000000	...	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
28	Runnymede, Swansea	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.00	0.000000	...	0.000000	0.000000	0.000000	0.00	0.030303	0.000000	0.000000	0.000000	0.000000	0.030303
29	St. James Town	0.000000	0.000000	0.000000	0.0	0.000000	0.037500	0.000000	0.00	0.012500	...	0.000000	0.000000	0.000000	0.00	0.012500	0.000000	0.000000	0.012500	0.000000	0.000000
30	St. James Town, Cabbagetown	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.00	0.000000	...	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
31	Stn A PO Boxes	0.000000	0.000000	0.000000	0.0	0.000000	0.010309	0.010309	0.00	0.020619	...	0.000000	0.000000	0.000000	0.00	0.010309	0.000000	0.000000	0.000000	0.000000	0.010309
32	Studio District	0.000000	0.000000	0.000000	0.0	0.000000	0.054054	0.000000	0.00	0.000000	...	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.027027
33	Summerhill West, Rathnelly, South Hill, Forest...	0.000000	0.000000	0.000000	0.0	0.000000	0.066667	0.000000	0.00	0.000000	...	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.066667	0.000000	0.000000	0.000000
34	The Annex, North Midtown, Yorkville	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.00	0.000000	...	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
35	The Beaches	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.00	0.000000	...	0.000000	0.000000	0.250000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
36	The Danforth West, Riverdale	0.000000	0.000000	0.000000	0.0	0.000000	0.023256	0.000000	0.00	0.000000	...	0.023256	0.000000	0.023256	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.023256
37	Toronto Dominion Centre, Design Exchange	0.000000	0.000000	0.000000	0.0	0.000000	0.030000	0.000000	0.00	0.010000	...	0.000000	0.000000	0.000000	0.01	0.010000	0.000000	0.000000	0.010000	0.000000	0.000000
38	University of Toronto, Harbord	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.00	0.000000	...	0.000000	0.000000	0.000000	0.00	0.000000	0.030303	0.000000	0.000000	0.000000	0.030303

39 rows × 234 columns

#

def return_most_common_venues(row, num_top_venues):
    row_categories = row.iloc[1:]
    row_categories_sorted = row_categories.sort_values(ascending=False)
    
    return row_categories_sorted.index.values[0:num_top_venues]

#
# Let's take only 7 as the density seems to be lower than in NY where value of 10 was used
#
num_top_venues = 7

indicators = ['st', 'nd', 'rd']

# create columns according to number of top venues
columns = ['Neighborhood']
for ind in np.arange(num_top_venues):
    try:
        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))
    except:
        columns.append('{}th Most Common Venue'.format(ind+1))

# create a new dataframe
neighborhoods_venues_sorted = pd.DataFrame(columns=columns)
neighborhoods_venues_sorted['Neighborhood'] = toronto_grouped['Neighborhood']

for ind in np.arange(toronto_grouped.shape[0]):
    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(toronto_grouped.iloc[ind, :], num_top_venues)

neighborhoods_venues_sorted.head()

	Neighborhood	1st Most Common Venue	2nd Most Common Venue	3rd Most Common Venue	4th Most Common Venue	5th Most Common Venue	6th Most Common Venue	7th Most Common Venue
0	Berczy Park	Coffee Shop	Cocktail Bar	Beer Bar	Cheese Shop	Bakery	Restaurant	Farmers Market
1	Brockton, Parkdale Village, Exhibition Place	Café	Breakfast Spot	Nightclub	Coffee Shop	Pet Store	Climbing Gym	Burrito Place
2	Business reply mail Processing Centre, South C...	Light Rail Station	Auto Workshop	Pizza Place	Comic Shop	Restaurant	Burrito Place	Brewery
3	CN Tower, King and Spadina, Railway Lands, Har...	Airport Service	Airport Lounge	Airport Terminal	Airport	Boutique	Sculpture Garden	Plane
4	Central Bay Street	Coffee Shop	Sandwich Place	Café	Italian Restaurant	Burger Joint	Bubble Tea Shop	Salad Place

Cluster Neighborhoods

# set number of clusters
kclusters = 5

toronto_grouped_clustering = toronto_grouped.drop('Neighborhood', 1)

# run k-means clustering
kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(toronto_grouped_clustering)

# check cluster labels generated for each row in the dataframe
kmeans.labels_[0:20] 

array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1],
      dtype=int32)

# add clustering labels
neighborhoods_venues_sorted_w_clusters = neighborhoods_venues_sorted.copy()
neighborhoods_venues_sorted_w_clusters.insert(0, 'Cluster Labels', kmeans.labels_)

toronto_merged = df31.copy()
toronto_merged.rename(columns={"Neighbourhood": "Neighborhood"}, inplace=True)

# merge manhattan_grouped with manhattan_data to add latitude/longitude for each neighborhood
toronto_merged = toronto_merged.join(neighborhoods_venues_sorted_w_clusters.set_index('Neighborhood'), on='Neighborhood')

Visualize

# create map
map_clusters = folium.Map(location=[df31.loc[2, 'Latitude'], df31.loc[2, 'Longitude']], zoom_start=11)

# set color scheme for the clusters
x = np.arange(kclusters)
ys = [i + x + (i*x)**2 for i in range(kclusters)]
colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))
rainbow = [colors.rgb2hex(i) for i in colors_array]

# add markers to the map
markers_colors = []
for lat, lon, poi, cluster in zip(toronto_merged['Latitude'], toronto_merged['Longitude'], toronto_merged['Neighborhood'], toronto_merged['Cluster Labels']):
    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)
    folium.CircleMarker(
        [lat, lon],
        radius=5,
        popup=label,
        color=rainbow[cluster-1],
        fill=True,
        fill_color=rainbow[cluster-1],
        fill_opacity=0.7).add_to(map_clusters)
       
map_clusters

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Examine Clusters

toronto_merged.head(50)

	Postal Code	Borough	Neighborhood	Latitude	Longitude	Cluster Labels	1st Most Common Venue	2nd Most Common Venue	3rd Most Common Venue	4th Most Common Venue	5th Most Common Venue	6th Most Common Venue	7th Most Common Venue
2	M5A	Downtown Toronto	Regent Park, Harbourfront	43.654260	-79.360636	1	Coffee Shop	Café	Park	Bakery	Breakfast Spot	Pub	Theater
4	M7A	Downtown Toronto	Queen's Park, Ontario Provincial Government	43.662301	-79.389494	1	Coffee Shop	Sushi Restaurant	Bank	Bar	Portuguese Restaurant	Beer Bar	Diner
9	M5B	Downtown Toronto	Garden District, Ryerson	43.657162	-79.378937	1	Coffee Shop	Clothing Store	Japanese Restaurant	Hotel	Bubble Tea Shop	Middle Eastern Restaurant	Cosmetics Shop
15	M5C	Downtown Toronto	St. James Town	43.651494	-79.375418	1	Coffee Shop	Café	Gastropub	Cocktail Bar	American Restaurant	Italian Restaurant	Hotel
19	M4E	East Toronto	The Beaches	43.676357	-79.293031	1	Health Food Store	Pub	Trail	Yoga Studio	Dog Run	Dessert Shop	Diner
20	M5E	Downtown Toronto	Berczy Park	43.644771	-79.373306	1	Coffee Shop	Cocktail Bar	Beer Bar	Cheese Shop	Bakery	Restaurant	Farmers Market
24	M5G	Downtown Toronto	Central Bay Street	43.657952	-79.387383	1	Coffee Shop	Sandwich Place	Café	Italian Restaurant	Burger Joint	Bubble Tea Shop	Salad Place
25	M6G	Downtown Toronto	Christie	43.669542	-79.422564	1	Grocery Store	Café	Park	Nightclub	Coffee Shop	Italian Restaurant	Baby Store
30	M5H	Downtown Toronto	Richmond, Adelaide, King	43.650571	-79.384568	1	Coffee Shop	Café	Restaurant	Clothing Store	Deli / Bodega	Hotel	Gym
31	M6H	West Toronto	Dufferin, Dovercourt Village	43.669005	-79.442259	1	Pharmacy	Bakery	Middle Eastern Restaurant	Grocery Store	Park	Bar	Supermarket
36	M5J	Downtown Toronto	Harbourfront East, Union Station, Toronto Islands	43.640816	-79.381752	1	Coffee Shop	Aquarium	Café	Hotel	Brewery	Scenic Lookout	Italian Restaurant
37	M6J	West Toronto	Little Portugal, Trinity	43.647927	-79.419750	1	Bar	Coffee Shop	Asian Restaurant	Vietnamese Restaurant	Restaurant	Vegetarian / Vegan Restaurant	Café
41	M4K	East Toronto	The Danforth West, Riverdale	43.679557	-79.352188	1	Greek Restaurant	Coffee Shop	Italian Restaurant	Bookstore	Ice Cream Shop	Furniture / Home Store	Liquor Store
42	M5K	Downtown Toronto	Toronto Dominion Centre, Design Exchange	43.647177	-79.381576	1	Coffee Shop	Hotel	Café	Salad Place	American Restaurant	Japanese Restaurant	Italian Restaurant
43	M6K	West Toronto	Brockton, Parkdale Village, Exhibition Place	43.636847	-79.428191	1	Café	Breakfast Spot	Nightclub	Coffee Shop	Pet Store	Climbing Gym	Burrito Place
47	M4L	East Toronto	India Bazaar, The Beaches West	43.668999	-79.315572	1	Fast Food Restaurant	Sushi Restaurant	Pub	Liquor Store	Sandwich Place	Burrito Place	Board Shop
48	M5L	Downtown Toronto	Commerce Court, Victoria Hotel	43.648198	-79.379817	1	Coffee Shop	Restaurant	Hotel	Café	Italian Restaurant	Gym	American Restaurant
54	M4M	East Toronto	Studio District	43.659526	-79.340923	1	Coffee Shop	American Restaurant	Bakery	Brewery	Café	Gastropub	Yoga Studio
61	M4N	Central Toronto	Lawrence Park	43.728020	-79.388790	1	Park	Bus Line	Business Service	Swim School	Yoga Studio	Dessert Shop	Escape Room
62	M5N	Central Toronto	Roselawn	43.711695	-79.416936	0	Ice Cream Shop	Home Service	Garden	Health & Beauty Service	Dessert Shop	Diner	Discount Store
67	M4P	Central Toronto	Davisville North	43.712751	-79.390197	1	Gym / Fitness Center	Hotel	Pizza Place	Department Store	Sandwich Place	Breakfast Spot	Food & Drink Shop
68	M5P	Central Toronto	Forest Hill North & West, Forest Hill Road Park	43.696948	-79.411307	3	Jewelry Store	Trail	Mexican Restaurant	Sushi Restaurant	Yoga Studio	Department Store	Escape Room
69	M6P	West Toronto	High Park, The Junction South	43.661608	-79.464763	1	Thai Restaurant	Mexican Restaurant	Café	Discount Store	Italian Restaurant	Bar	Fried Chicken Joint
73	M4R	Central Toronto	North Toronto West, Lawrence Park	43.715383	-79.405678	1	Clothing Store	Coffee Shop	Yoga Studio	Sporting Goods Shop	Grocery Store	Ice Cream Shop	Fast Food Restaurant
74	M5R	Central Toronto	The Annex, North Midtown, Yorkville	43.672710	-79.405678	1	Café	Sandwich Place	Coffee Shop	Liquor Store	Indian Restaurant	Pub	BBQ Joint
75	M6R	West Toronto	Parkdale, Roncesvalles	43.648960	-79.456325	1	Breakfast Spot	Gift Shop	Restaurant	Cuban Restaurant	Eastern European Restaurant	Dog Run	Italian Restaurant
79	M4S	Central Toronto	Davisville	43.704324	-79.388790	1	Sandwich Place	Dessert Shop	Café	Italian Restaurant	Sushi Restaurant	Gym	Coffee Shop
80	M5S	Downtown Toronto	University of Toronto, Harbord	43.662696	-79.400049	1	Café	Bookstore	Bar	Italian Restaurant	Japanese Restaurant	Bakery	Dessert Shop
81	M6S	West Toronto	Runnymede, Swansea	43.651571	-79.484450	1	Coffee Shop	Café	Sushi Restaurant	Italian Restaurant	Pub	Latin American Restaurant	Bar
83	M4T	Central Toronto	Moore Park, Summerhill East	43.689574	-79.383160	2	Summer Camp	Yoga Studio	Farmers Market	Event Space	Ethiopian Restaurant	Escape Room	Electronics Store
84	M5T	Downtown Toronto	Kensington Market, Chinatown, Grange Park	43.653206	-79.400049	1	Café	Coffee Shop	Vietnamese Restaurant	Vegetarian / Vegan Restaurant	Mexican Restaurant	Caribbean Restaurant	Grocery Store
86	M4V	Central Toronto	Summerhill West, Rathnelly, South Hill, Forest...	43.686412	-79.400049	1	Coffee Shop	Bagel Shop	Pizza Place	Supermarket	Sushi Restaurant	Light Rail Station	Fried Chicken Joint
87	M5V	Downtown Toronto	CN Tower, King and Spadina, Railway Lands, Har...	43.628947	-79.394420	1	Airport Service	Airport Lounge	Airport Terminal	Airport	Boutique	Sculpture Garden	Plane
91	M4W	Downtown Toronto	Rosedale	43.679563	-79.377529	4	Park	Playground	Trail	Yoga Studio	Deli / Bodega	Escape Room	Electronics Store
92	M5W	Downtown Toronto	Stn A PO Boxes	43.646435	-79.374846	1	Coffee Shop	Cocktail Bar	Seafood Restaurant	Italian Restaurant	Hotel	Japanese Restaurant	Restaurant
96	M4X	Downtown Toronto	St. James Town, Cabbagetown	43.667967	-79.367675	1	Restaurant	Pizza Place	Coffee Shop	Bakery	Pub	Pet Store	Italian Restaurant
97	M5X	Downtown Toronto	First Canadian Place, Underground city	43.648429	-79.382280	1	Coffee Shop	Café	Hotel	Restaurant	Gym	Japanese Restaurant	Deli / Bodega
99	M4Y	Downtown Toronto	Church and Wellesley	43.665860	-79.383160	1	Coffee Shop	Sushi Restaurant	Japanese Restaurant	Restaurant	Gay Bar	Fast Food Restaurant	Yoga Studio
100	M7Y	East Toronto	Business reply mail Processing Centre, South C...	43.662744	-79.321558	1	Light Rail Station	Auto Workshop	Pizza Place	Comic Shop	Restaurant	Burrito Place	Brewery

Results

Neighbourhoods are pretty similar in the central part of Toronto. Most of them have a wide variaty of coffe shops, restaraunts and other entertainment-related places. Business ventures are also highly popular, but are not widely represented in an available data set.